class_docstr: Transformer for binarizing categorical and ordinal (including continuous) features.
  
  Note that all features are converted into binary variables before learning Boolean rules.
docstr: Initialize transformer for binarizing categorical and ordinal (including continuous) features
  
  :param colCateg: List of categorical columns, defaults to [], 'object' dtype automatically treated as categorical
  :type colCateg: List[str], optional
  :param numThresh: Number of quantile thresholds to binarize ordinal features, defaults to 9
  :type numThresh: int, optional
  :param negations: Include negations, defaults to False
  :type negations: bool, optional
  :param threshStr: Convert thresholds to strings, defaults to False
  :type threshStr: bool, optional
  :param threshOverride: Dictionary to override quantile thresholds, defaults to {},
      formatted as `{colname : np.linspace object}` to define cuts
  :type threshOverride: Dict, optional
docstr_tok: ["Initialize","transformer","for","binarizing","categorical","and","ordinal","(","including","continuous",")","features",":","param","colCateg",":","List","of","categorical","columns",",","defaults","to","[","]",",","'object","'","dtype","automatically","treated","as","categorical",":","type","colCateg",":","List","[","str","]",",","optional",":","param","numThresh",":","Number","of","quantile","thresholds","to","binarize","ordinal","features",",","defaults","to","9",":","type","numThresh",":","int",",","optional",":","param","negations",":","Include","negations",",","defaults","to","False",":","type","negations",":","bool",",","optional",":","param","threshStr",":","Convert","thresholds","to","strings",",","defaults","to","False",":","type","threshStr",":","bool",",","optional",":","param","threshOverride",":","Dictionary","to","override","quantile","thresholds",",","defaults","to","{","}",",","formatted","as","`","{","colname",":","np.linspace","object","}","`","to","define","cuts",":","type","threshOverride",":","Dict",",","optional"]
code: def __init__(
      self,
      colCateg: List[str] = [],
      numThresh: int = 9,
      negations: bool = False,
      threshStr: bool = False,
      threshOverride: Dict = {},
      **kwargs,
  ):
      """
      Initialize transformer for binarizing categorical and ordinal (including continuous) features
  
      :param colCateg: List of categorical columns, defaults to [], 'object' dtype automatically treated as categorical
      :type colCateg: List[str], optional
      :param numThresh: Number of quantile thresholds to binarize ordinal features, defaults to 9
      :type numThresh: int, optional
      :param negations: Include negations, defaults to False
      :type negations: bool, optional
      :param threshStr: Convert thresholds to strings, defaults to False
      :type threshStr: bool, optional
      :param threshOverride: Dictionary to override quantile thresholds, defaults to {},
          formatted as `{colname : np.linspace object}` to define cuts
      :type threshOverride: Dict, optional
      """
      # List of categorical columns
      if type(colCateg) is pd.Series:
          self.colCateg = colCateg.tolist()
      elif type(colCateg) is not list:
          self.colCateg = [colCateg]
      else:
          self.colCateg = colCateg
  
      self.threshOverride = (
          {} if threshOverride is None else threshOverride
      )
      # Number of quantile thresholds used to binarize ordinal features
      self.numThresh = numThresh
      self.thresh: Dict[str, np.ndarray] = {}
      # whether to append negations
      self.negations = negations
      # whether to convert thresholds on ordinal features to strings
      self.threshStr = threshStr
  
file_path: dowhy/dowhy/causal_refuters/overrule/BCS/load_process_data_BCS.py
imports: from typing import Dict, List
  import numpy
  import pandas
  from sklearn.base import TransformerMixin
  from sklearn.preprocessing import OneHotEncoder
label_desc: Use image node_id 1 to create a new FeatureBinarizer object from inherited base classes: TransformerMixin with example: obj = FeatureBinarizer(colCateg, numThresh, negations, threshStr, threshOverride)